GIANT MOLECULAR CLOUD FORMATION IN DISK GALAXIES: CHARACTERIZING SIMULATED VERSUS OBSERVED CLOUD CATALOGS
Bibliographic record
Abstract
We present the results of a study of simulated giant molecular clouds (GMCs) formed in a Milky Way–type galactic disk with a flat rotation curve. This simulation, which does not include star formation or feedback, produces clouds with masses ranging between 10 4 M ☉ and 10 7 M ☉ . We compare our simulated cloud population to two observational surveys: the Boston University-Five College Radio Astronomy Observatory Galactic Ring Survey and the BIMA All-Disk Survey of M33. An analysis of the global cloud properties as well as a comparison of Larson's scaling relations is carried out. We find that simulated cloud properties agree well with the observed cloud properties, with the closest agreement occurring between the clouds at comparable resolution in M33. Our clouds are highly filamentary—a property that derives both from their formation due to gravitational instability in the sheared galactic environment, as well as to cloud–cloud gravitational encounters. We also find that the rate at which potentially star-forming gas accumulates within dense regions—wherein n thresh ⩾ 10 4 cm −3 —is 3% per 10 Myr, in clouds of roughly 10 6 M ☉ . This suggests that star formation rates in observed clouds are related to the rates at which gas can be accumulated into dense subregions within GMCs via filamentary flows. The most internally well-resolved clouds are chosen for listing in a catalog of simulated GMCs—the first of its kind. The cataloged clouds are available as an extracted data set from the global simulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".